Небольшая статья о том, как впилить proemtheus метрики в ваш любимый ML, на самом деле подойдет в качестве примера для любого batch приложения на python https://medium.com/under-the-microscope/prometheus-metrics-for-batch-jobs-on-kubernetes-b06ec3b620bc #python #prometheus #k8s

🐍 И вот вам ещё утилита pipx, которая позволяет питонобинарники изолировать в песочнице и запускать их оттуда - https://github.com/pipxproject/pipx

#python #pipx #github

HTTP Debugging for Python

Instantly view & debug all HTTP traffic from any #python tool, script, or server

​​Simple real time visualisation of the execution of a #python program: https://github.com/alexmojaki/heartrate

python-prompt-toolkit -- штука для создания всяких интерактивных CLI, REPLов и всего такого. Есть подсветка синтаксиса с помощью pygments, автодополнение и всё такое. На нём работает pgcli, ipython, gitsome (умный автокомплит для git), EdgeDB, pyvim (vim на Python, просто потому что) и много что ещё.

#python

Modules and Packages: Live and Let Die! — David Beasley's insane three-hour presentation about the construction of modules, packages and import systems in Python. Everything is pretty comprehensive, from the very basics to the internals with examples.

Recommended👌

#python

You can start a full AWS-ish copy locally with just a single command: docker-compose up

Not sure about why would you need to do it, though.

https://github.com/localstack/localstack

#python #java

​​A one-click database. No server required.

https://easydb.io/

Clients exist for #js, #python, #ruby, and #shell

Новый курс на Coursera от Google про автоматизацию различных операций с помощью Python.

http://amp.gs/ulnc
#coursera #automation #python

Наткнулся на интересное #бэкап-решение.
Умеет в #s3, дедупликацию, интегрируется с #k8s и #ceph, но также и бэкапить обычные блочные устройства.
OpenSource, написанно на #python.

https://benji-backup.me/

​​Great Expectations: Always know what to expect from your data.

Great Expectations helps data teams eliminate pipeline debt, through data testing, documentation, and profiling.

Software developers have long known that testing and documentation are essential for managing complex codebases. Great Expectations brings the same confidence, integrity, and acceleration to data science and data engineering teams.

See Down with Pipeline Debt! for an introduction to the philosophy of pipeline testing: https://medium.com/@expectgreatdata/down-with-pipeline-debt-introducing-great-expectations-862ddc46782a

Key features:
- Expectations or assertions for data. They are the workhorse abstraction in Great Expectations, covering all kinds of common data issues
- Batteries-included data validation
- Tests are docs and docs are tests: many data teams struggle to maintain up-to-date data documentation. Great Expectations solves this problem by rendering Expectations directly into clean, human-readable documentation
- Automated data profiling: wouldn't it be great if your tests could write themselves? Run your data through one of Great Expectations' data profilers and it will automatically generate Expectations and data documentation
- Pluggable and extensible

https://github.com/great-expectations/great_expectations

#python #ds #docops

Improved colored diff
#python #tools #text
https://github.com/jeffkaufman/icdiff